Revenue management has, in the past few years, shifted from being a manufacturer-centric discipline to one embracing the supply chain – manufacturers, retailers, wholesalers and distributors. Technology and the Internet of Things (IoT) are speeding up and enhancing revenue management benefits for all parties.
Below we outline a couple of ways in which this is occurring.
Revenue management systemically applies analytics across the supply chain to create, capture and retain sustainable value for consumers, shoppers, retailers and manufacturers. Oriented around incremental volume, revenue and margin growth, range optimization and promotional planning, typically its scope includes all trade facing investment across the marketing mix including brands, products, packs, channels, consumption and shopping occasions; investment required to execute such as trading terms, promotional displays, sales force; and pricing architecture. It takes into account channel, geography, route to market, and trading up (premiumization) or down (private label).
Technology, including tools such as blockchain, enables better visibility of the supply chain. One way in which this is evidencing itself is in three-way P&Ls – the manufacturer’s, the retailer’s, including net margins and fully cost retail price to list price, including cost of inventory and working capital considerations, and the distributor or wholesaler, who in some cases is taking a margin on cartons moved, and in some instances may be a fully ‘loaded’ service provider with its own sales force, logistics and network.
Technology is also allowing better visibility across the trade promotions management system of Plan > Execute > Monitor > Optimize.
At the promotions Planning stage, the ability to see financial measures such as Gross Margin at a channel and customer level is required. Along with the ability to view annualized, monthly and weekly promotions.
All aspects of managing customers, forecast volumes, predictive analytics based on history and promotional objectives and mechanics, and scenario planning and ‘what ifs’ pre-promotion are simulated and captured. Financial governance rules and protocols are set to manage variances, scenario planning and constraints, such as maximum discount, standard costs, maximum number of promotions. A good revenue management system then converts the pre-plan into final confirmed plan with ROI metrics set in place, linked to ROI efficiency and effectiveness, managing incremental contribution and net sales against incremental trade spend.
As a consideration, whilst forecasting is key and requires constant new modelling and algorithm development, AI and machine learning capabilities will have been impacted by COVID-19. They may struggle with major disruptions to historical data patterns, so manufacturers and retailers need to take this into account when forecasting promotion impacts.
At the promotion Execution stage, technology enables manufacturers to ensure the promotional plan is populated and automatically distributed to the sales and distributor teams, and events are lined up with activity to ensure maximum impact in a series of simple dashboards. Relevant third party data sources are linked, with full promotional spend management and end-to-end accruals and claims automation.
From a promotion Monitoring standpoint, real-time dashboards and inflight management enable the ability to use multiple data sources and calculate different variable impacts on the promotion on the fly, changing SKUs, product groupings, customer buying groups, volume, phasing, impacts of cannibalization and sort term seasonality or external key event impacts. As well as real time changes to the forecast and any event that may cause out-of-stocks.
Sales force and field automation has been on an evolution curve in the past ten years.
In 2010, sales and field forces were performing primarily manual audits consisting of surveys, shelf audits, manual order entry and route planning.
Circa 2015, the market moved to systemized audits. Sales and field routing was optimized, basic store analytics came into play, performance indicators and ‘perfect store’ guidelines were introduced. These resulted in increased sales force productivity via:
From around 2015, sales forces began to implement automated audits incorporating image recognition, advanced route planning, prescriptive ordering and gamification. This reduced time in store, such as a sub-3 minute KPI compliance check with up to 98 per cent accuracy performed in a 60 – 80 per cent reduced time window. Competitive intelligence was enhanced, blind spots reduced, reporting and insights improved along with top-down demand planning integration due to improved forecasting, all resulting in a 3-5 per cent increase in sales … largely driven by KPI compliance.
So where are we now? Fast forward to 2021 and we’re in the world of predictive analytics, facial recognition smart shelves and robotics. Smart AI audits mean the ‘store of the future’ will be fully digitized using shelf edge cameras or robotics.
The growth of e-commerce – accelerated by the pandemic – has resulted in manufacturers having to satisfy different stock and supply requirements of global and local retail players. Direct-to-consumer (D2C) sales have created additional costs and operational pressure.
This has made forecasting more of an educated guess, as there is little or no history on which to base new assumptions (see previous point about AI and disruption to patterns). Different measures for success need to be developed as do cost and profit profiles by channel and product.
Ecommerce retailers have become bullish in their negotiations as their growth has given them more leverage. At the same time discounters have ‘suffered’ with fewer people shopping in store. During the pandemic, retailers have adopted policies to deal with social distancing and better hygiene protocols. The Grocer magazine indicated that Virtual Queuing has been introduced, on a trial basis at different UK retailers trying to fit the ‘new normal’ constraints. Customers select the store where they wish to shop and the app notifies them when they are at the ‘front’ of the line, without the need to physically queue. A bit like the Starbucks app for physical instore pickup.
From the few examples above, it can be observed that the discipline of revenue management, reliant as it is on data analytics, has only been enhanced by technology. This can only be expected to increase in the short to medium term.